论文标题
组合动态虚拟虚拟时空图映射用于流量预测
Combined Dynamic Virtual Spatiotemporal Graph Mapping for Traffic Prediction
论文作者
论文摘要
城市施工量表的持续扩展最近促进了对管理交通交叉点的动态的需求，使自适应模型成为一个热门话题。现有的深度学习方法具有强大的功能，可适应复杂的异质图。但是，它们仍然有缺点，可以将它们大致分为两类，1）时空异步模型方法分别考虑时间和空间依赖性，导致概括和较大的不稳定性，同时汇总； 2）由于局部接受场，时空同步模型很难捕获长期的时间依赖性。为了克服上述挑战，A \ textbf {c} ombined \ textbf {d} ynamic \ textbf {v} irtual spatiotemporal \ textbf {g} raph \ textbf {m textbf {m} apping \ textbf \ textbf {（cdvgm）}贡献如下：1）设计了动态虚拟图Laplacian（$ dvgl $），该图既考虑空间信号传递又同时考虑了时间特征； 2）长期的时间增强模型（$ lt^2s $），用于提高时间序列预测的稳定性；广泛的实验表明，CDVGM具有快速收敛速度和低资源消耗的出色性能，并且在准确性和概括方面都达到了当前的SOTA效应。该代码可在\ hyperlink {https://github.com/dandelionym/cdvgm。} {https://github.com/dandelionym/cdvgm。}中获得。
The continuous expansion of the urban construction scale has recently contributed to the demand for the dynamics of traffic intersections that are managed, making adaptive modellings become a hot topic. Existing deep learning methods are powerful to fit complex heterogeneous graphs. However, they still have drawbacks, which can be roughly classified into two categories, 1) spatiotemporal async-modelling approaches separately consider temporal and spatial dependencies, resulting in weak generalization and large instability while aggregating; 2) spatiotemporal sync-modelling is hard to capture long-term temporal dependencies because of the local receptive field. In order to overcome above challenges, a \textbf{C}ombined \textbf{D}ynamic \textbf{V}irtual spatiotemporal \textbf{G}raph \textbf{M}apping \textbf{(CDVGM)} is proposed in this work. The contributions are the following: 1) a dynamic virtual graph Laplacian ($DVGL$) is designed, which considers both the spatial signal passing and the temporal features simultaneously; 2) the Long-term Temporal Strengthen model ($LT^2S$) for improving the stability of time series forecasting; Extensive experiments demonstrate that CDVGM has excellent performances of fast convergence speed and low resource consumption and achieves the current SOTA effect in terms of both accuracy and generalization. The code is available at \hyperlink{https://github.com/Dandelionym/CDVGM.}{https://github.com/Dandelionym/CDVGM.}
